Abstract
This paper concerns the problem of enhanced results on robust finite time passivity for uncertain discrete time Markovian jumping BAM delayed neural networks with leakage delay. By implementing a proper Lyapunov–Krasovskii functional candidate, reciprocally convex combination method, and linear matrix inequality technique, we derive several sufficient conditions for varying the passivity of discrete time BAM neural networks. Further, some sufficient conditions for finite time boundedness and passivity for uncertainties are proposed by employing zero inequalities. Finally, the enhancement of the feasible region of the proposed criteria is shown via numerical examples with simulation to illustrate the applicability and usefulness of the proposed method.
Highlights
Introduction and problem statement with preliminariesThere has been a growing research interest in the field of recurrent neural networks (RNNs) largely studied by many researchers in recent years
Our main objective of this paper is to study the delay-dependent exponential stability problem for a class of Markovian jumping uncertain bidirectional associative memory (BAM) neural networks with mixed time delays, leakage delays, and α-inverse Holder activation functions under stochastic noise perturbation
To the best of authors knowledge, so far, no result on the global exponential stability of Markovian jumping stochastic impulsive uncertain BAM neural networks with leakage, mixed time delays, and α-inverse Hölder activation functions has been available in the existing literature, which motivates our research to derive the following BAM neural networks: t dx(t) = –Cx(t – ν1) + W0f y(t) + W1g y t – τ1(t) + W2 h y(s) ds + I dt t–σ1
Summary
Introduction and problem statement with preliminariesThere has been a growing research interest in the field of recurrent neural networks (RNNs) largely studied by many researchers in recent years. Our main objective of this paper is to study the delay-dependent exponential stability problem for a class of Markovian jumping uncertain BAM neural networks with mixed time delays, leakage delays, and α-inverse Holder activation functions under stochastic noise perturbation.
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